What is a ReAct trace?

A ReAct trace is a log of the Reasoning and Acting steps an agent takes to complete a task. This MCP analyzes the text within those specific phases.

How does this MCP detect zero-shot behavior?

It uses the analyze_trace_verbosity capability to check if the thought block is too brief relative to the action, which indicates the agent skipped reasoning.

Which AI clients can use this MCP?

You can use this with any MCP-compatible client like Claude, Cursor, Windsurf, or VS Code.

Can I analyze multiple traces at once?

Yes, the aggregate_trace_metrics capability is designed to summarize reasoning patterns across entire sets of traces.

What is the verbosity ratio?

It is the mathematical ratio between the number of words in the 'Thought' phase and the number of words in the 'Action' phase.

What is a zero-shot indicator in this context?

A zero-shot indicator is triggered when the analyze_trace_verbosity capability finds that the thought block contains fewer than 10 words, suggesting the model skipped detailed reasoning.

How is the verbosity ratio calculated?

The verbosity ratio is the total word count of the thought block divided by the total word count of the action block.

Can I summarize multiple steps at once?

Yes, you can use aggregate_trace_metrics to get a high-level summary of total steps, average verbosity, and zero-shot counts for a collection of analysis results.

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Connect Reasoning Step Word Count Analyzer to your AI

Set it up once and it works in ChatGPT, Claude, Cursor, and any other client you love. It all runs safely in the background, so you can relax and focus on what really matters. Audit agent reasoning depth and detect zero-shot behavior.

Ask AI about this Connector

Developed, maintained, and hosted by Vinkius.

MCP VERIFIED · PRODUCTION READY · VINKIUS GUARANTEED

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Works with modern AI clients that support MCP, including ChatGPT, Claude, Cursor, and more.

Your AI does the work. We keep it safe and sound.

No other AI tool gives you this. Your workflow runs in the client you already love, your account stays protected, and you decide what the agent can and cannot do. It is the whole reason to make the switch.

Each Connector lives inside our infrastructure, the same place where your workflow runs. The three things below are already part of it, working quietly in the background so you never have to think about them. Nothing to add, nothing to set up, nothing to worry about.

V8 Isolate01

Each Connector runs inside its own sealed environment.

Each Connector runs inside its own sealed V8 runtime — no access to your files, your system, or other servers. A Connector never sees past its own request.

Kill Switch02

One switch stops every agent. Instantly.

One action deactivates every Connector, revokes every token, and ends every session at once. AI Governance keeps the record.

FinOps per User03

Spending limits that pause instead of surprise.

Every user keeps their own connections and credentials, isolated from everyone else. Anything that would cross your spending limit is held for your approval, not billed.

Three controls, built into the layer your AI connects through.

Curated set · 3 capabilities

We ship only the actions that matter.

Capability set01 / 01

01-03

3 capabilities in this set.

Part of 3 available through Reasoning Step Word Count Analyzer.

01
Aggregate trace metrics
This capability summarizes reasoning patterns across entire sets of traces. It provides a high-level view of how your agent's reasoning behavior changes over time.
02
Analyze trace verbosity
This capability calculates the verbosity ratio between thought and action phases. It helps you identify zero-shot behavior when the thought block is too short.
03
Identify high verbosity steps
This capability finds specific moments in a trace where the agent shows high cognitive effort. It highlights steps with significant word counts in the reasoning phase.

Real work

What teams actually run

Debugging Agent Logic

Use the verbosity ratio to see if an agent is failing because it isn't 'thinking' enough before acting.

Prompt Optimization

Compare verbosity metrics before and after prompt changes to see if you've increased reasoning depth.

Workflow Auditing

Run aggregate metrics on a batch of traces to ensure consistent agent behavior.

Model Comparison

Analyze how different models distribute word counts between thought and action phases.

One connector, every AI

Reasoning Step Word Count Analyzer works with the most popular AI clients.

These are the most popular clients, each with a step-by-step guide: one link, set up once, with governance and visibility built in. And because everything runs on the MCP standard, the same connection also works in any other compatible client — nothing to rebuild.

Building your own app? The connector is yours to use.

You don't need a client to put Reasoning Step Word Count Analyzer to work: the same hosted connection plugs into your own applications and agent code, with the same governance on every request. Build with it, chat with it — one connection for both.

Observed, not estimated

875ms average. Fast in production.

Reasoning Step Word Count Analyzer is checked daily against the live service.

Daily averagePeak 1049ms

Oct 3Today

Fastest day: 754ms
Slowest day: 1049ms
14-day trend: Stable0%

Connect your client

One URL. Every client.

Activate the Connector, copy your link, and paste it into the client you already use. 3 capabilities arrive ready to run.

Preview access · not provider authentication

The vk_preview_* token belongs to Vinkius preview infrastructure. It lets Claude discover and display the capabilities of Reasoning Step Word Count Analyzer, so you can see the experience inside your AI.

It does not authenticate your account with Reasoning Step Word Count Analyzer. Actions requiring credentials or live account data may not run until you activate the Connector and authorize the service.

Reasoning Step Word Count Analyzer Connector

You're all set. Choose your MCP client and follow the setup instructions.

Connector linkhttps://edge.vinkius.com/vk_preview_ekv3hS0trxu4SmkQGQPI5KigM6tgHZpfvfQmz7Gj/mcp

Claude Desktop

Follow the steps below to connect in seconds.

1In Claude Desktop, open Settings → Connectors.
2Click “Add custom connector” and paste the connector link above as the remote MCP server URL.
3Click Add and start a new chat — Reasoning Step Word Count Analyzer capabilities are ready to use.

Configuration · claude_desktop_config.jsonCopy

{
  "mcpServers": {
    "reasoning-step-word-count-analyzer-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_ekv3hS0trxu4SmkQGQPI5KigM6tgHZpfvfQmz7Gj/mcp"
    }
  }
}

Claude
ChatGPT
Cursor
VS Code
Windsurf
Claude Code
JetBrains
Cline

Step-by-step instructions for each client are in the guide. How to connect

Guided setup for Claude? Set up Reasoning Step Word Count Analyzer in Claude and other AIs

See all the AI clients this connector works with ↑

Who it's for

Built for the work Reasoning Step Word Count Analyzer owners hand off.

This MCP is built for developers and researchers working with agentic workflows and ReAct-based models.

01
AI Engineers
They hand off ReAct traces to the MCP to debug agentic logic and reasoning failures.
02
LLM Researchers
They use the metrics to study how different models handle reasoning versus acting.
03
Prompt Engineers
They use verbosity data to refine prompts that encourage deeper reasoning.

FAQ

Questions Reasoning Step Word Count Analyzer owners ask.

01
What is a ReAct trace?
A ReAct trace is a log of the Reasoning and Acting steps an agent takes to complete a task. This MCP analyzes the text within those specific phases.
02
How does this MCP detect zero-shot behavior?
It uses the analyze_trace_verbosity capability to check if the thought block is too brief relative to the action, which indicates the agent skipped reasoning.
03
Which AI clients can use this MCP?
You can use this with any MCP-compatible client like Claude, Cursor, Windsurf, or VS Code.
04
Can I analyze multiple traces at once?
Yes, the aggregate_trace_metrics capability is designed to summarize reasoning patterns across entire sets of traces.
05
What is the verbosity ratio?
It is the mathematical ratio between the number of words in the 'Thought' phase and the number of words in the 'Action' phase.
06
What is a zero-shot indicator in this context?
A zero-shot indicator is triggered when the analyze_trace_verbosity capability finds that the thought block contains fewer than 10 words, suggesting the model skipped detailed reasoning.
07
How is the verbosity ratio calculated?
The verbosity ratio is the total word count of the thought block divided by the total word count of the action block.
08
Can I summarize multiple steps at once?
Yes, you can use aggregate_trace_metrics to get a high-level summary of total steps, average verbosity, and zero-shot counts for a collection of analysis results.

Explore

More in Developer Tools

Connect Reasoning Step Word Count Analyzer to Claude, Cursor, ChatGPT & more

Your AI does the work. We keep it safe and sound.

Each Connector runs inside its own sealed environment.

One switch stops every agent. Instantly.

Spending limits that pause instead of surprise.

We ship only the actions that matter.

01-03

Aggregate trace metrics

Analyze trace verbosity

Identify high verbosity steps

What teams actually run

Debugging Agent Logic

Prompt Optimization

Workflow Auditing

Model Comparison

Building your own app? The connector is yours to use.

Reasoning Step Word Count Analyzer Connector

Claude Desktop

AI Engineers

LLM Researchers

Prompt Engineers

What is a ReAct trace?

How does this MCP detect zero-shot behavior?

Which AI clients can use this MCP?

Can I analyze multiple traces at once?

What is the verbosity ratio?

What is a zero-shot indicator in this context?

How is the verbosity ratio calculated?

Can I summarize multiple steps at once?

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